The AAAI-17 Workshop on Knowledge-based Techniques for Problem Solving and Reasoning

Despite recent attempts in various subareas of AI to integrate technologies to solve complex problems such as autonomous cars, there are still gaps between research communities that prevent efficient transfer of knowledge. For example, knowledge representation techniques focus on formal semantics and flexibility of modeling frameworks and put less emphasis on actual problem solving that requires efficient tools. Other communities such as planning and search put emphasis on efficiency of problem solving, but less attention is given to how the real problem is modeled, the connection between modeling and efficiency of problem solving, and the capability of the models to support other important features like plan revision and adaptation. This workshop attempts to bridge these particular communities with the goal to exchange information leading to more efficient problem solving starting with the problem requirements and finishing with the solved problem.

Formal problem modeling is a critical step during problem solving. A good modeling framework should be flexible enough to describe important properties of problems solved and should allow application of efficient problem solving techniques. This workshop attracts papers at the frontier between formal problem modeling and problem solving. Papers should see the formal models from the perspective of problem solving and vice versa — problem solving techniques are seen in relation to models of the problem. For example, the paper can discuss the relation between planning domain models and planning algorithms or show how to enhance the domain model by extra information such as control knowledge. Papers discussing methods on how to obtain information that is useful for efficient problem solving are welcome.

We are in particular interested in papers addressing some of the following questions. How do the formal models relate to efficiency of problem solving? How do various modeling frameworks compare from the perspective of problem solving? How can the model be acquired? How can the model be verified and validated? How can the formal model be reformulated to get an efficiently-solvable model? How can the solution be checked with respect to the model? How does the model evolve in time? How can the model support solution revisions at execution time?

Application papers are also welcome, if they highlight the relation between the formal model of the problem and the solving approach. Description of specific models for specific problems is also possible, if the particular modeling techniques are studied from the perspective of problem solving.

Location: Union Square 3/4 on the Fourth Floor of the Hotel

08:50 – 09:00	Welcome and opening notes
09:00 – 10:30	Epistemic Specifications and Conformant Planning Yan Zhang and Yuanlin Zhang
	Initial State Prediction in Planning Senka Krivic, Michael Cashmore, Bram Ridder, Daniele Magazzeni, Sandor Szedmak and Justus Piater
	Automatic Extraction of Axioms for Planning Shuwa Miura and Alex Fukunaga
10:30 – 11:00	Coffee break
11:00 – 12:30	What is Going on: Utility-based Plan Selection in BDI Agents Ameneh Deljoo, Tom van Engers, Leon Gommans and Cees de Laat
	On Inductive Learning of Causal Knowledge for Problem Solving Seng-Beng Ho and Fiona Liausvia
	Causal Learning vs Reinforcement Learning for Knowledge Learning and Problem Solving Seng-Beng Ho
12:30 – 14:00	Lunch break (on your own)
14:00 – 15:30	On Automated Defeasible Reasoning with Controlled Natural Language and Argumentation Hannes Strass and Adam Wyner
	T2KG: An End-to-End System for Creating Knowledge Graph from Unstructured Text Natthawut Kertkeidkachorn and Ryutaro Ichise
	Learning Knowledge Representation Across Knowledge Graphs Pengshan Cai, Wei Li, Yansong Feng, Yuanzhuo Wang, Yantao Jia (presented by Shuo Yang)
15:30 – 16:00	Coffee break
16:00 – 17:30	Context Recognition in Multiple Occupants Situations: Detecting the Number of Agents in a Smart Home Environment with Pervasive Sensors Jennifer Renoux, Marjan Alirezaie, Lars Karlsson, Uwe Köckemann, Federico Pecora, and Amy Loutfi
	Model Selection with Nonlinear Embedding for Unsupervised Domain Adaptation Hemanth Venkateswara, Shayok Chakraborty, and Sethuraman Panchanathan
	Knowledge-based Morphological Classification of Galaxies from Vision Features Devendra Dhami, Sriraam Natarajan, and David Leake

Submitted papers must be formatted according to AAAI guidelines and submitted electronically through the KnowProS 2017 paper submission site. Authors are required to submit their electronic papers in PDF format. Submitted technical papers are expected to have 5-8 pages in total (if the length of your paper is very different from this range, please contact the organizers prior submission). Include your names and affiliations in the submission.

In any case, please submit the title and the abstract of your paper by the submission deadline and if you need later submission please contact the workshop organizers.

At least one author of each accepted paper is required to attend the workshop to present the work. Proceedings of the workshop will be available as a AAAI technical report (informal, archival publication, with an ISBN, freely available in AAAI's digital library).

Knowledge-based techniques for problem solving and reasoning (KnowProS 2017)

Knowledge-based techniques for problem solving and reasoning
(KnowProS 2017)